Contact Dr Stephen King
- Email: S.P.King@cranfield.ac.uk
- ORCID
Areas of expertise
- Instrumentation, Sensors and Measurement Science
- Vehicle Health Management
Background
Steve is currently a part-time senior Lecturer in Advanced Analytics having recently retired from Rolls-Royce (April 2020) where he was an Engineering Associate Fellow and EHM Specialist working within the Rolls-Royce Digital organisation. During his 41-year career at Rolls-Royce he held positions within the Measurement Engineering group, Electronics and Measurement Techniques department, Strategic Research Centre, Business Process Improvement Centre, Controls Engineering and System Design Engineering. Prior to this he worked for Electronic Flow Meters where he was responsible for the test and commissioning of flow measurement systems in the oil and gas industry.
His main interests is in the use of data mining and advanced analytical techniques for asset health monitoring applications. Steve holds a degree in Mathematics and Computer Science and a PhD in the application of expert systems for vibration analysis. In addition to being a Chartered Engineer, he is a Fellow member of both the Institution of Engineering and Technology and the Institute of Mathematics and its Applications.
Publications
Articles In Journals
- Hullait H, Leslie D, Pavlidis N & King S (2020) Robust function-on-function regression, Technometrics, Available online 29 July 2020.
- King S, Flint P & Sundaram S (2010) Handling sparse data problems in the context of monitoring multiple parameters in complex systems, Insight: Non-Destructive Testing & Condition Monitoring, 52 (8) 424-436.
- King S, Bannister P, Clifton D & Tarassenko L (2009) Probabilistic approach to the condition monitoring of aerospace engines, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 223 (5) 533-541.
- Hayton P, Utete S, King DM, King SP, Anuzis P & Tarassenko L (2007) Static and dynamic novelty detection methods for jet engine health monitoring, Philosophical Transactions A: Mathematical, Physical and Engineering Sciences, 365 (1851) 493-514.
- Nairac A, Townsend N, Carr R, King SP, Cowley P & Tarassenko L (1999) A system for the analysis of jet engine vibration data, Integrated Computer-Aided Engineering, 6 (1) 53-66.
- Allwood RJ, King SP & Pitts NJ (1996) The automatic interpretation of vibration data from gas turbines, The Aeronautical Journal, 100 (993) 99-107.
Conference Papers
- Fu R, Harrison R, King S & Mills A (2016) Lean burn combustion monitoring strategy based on data modelling. In: 2016 IEEE Aerospace Conference, Big Sky, MT, 5-12 March 2016.
- Clifton DA, McGrogan N, Tarassenko L, King D, King S & Anuzis P (2008) Bayesian extreme value statistics for novelty detection in gas-turbine engines. In: 2008 IEEE Aerospace Conference, Big Sky, MT, 1-8 March 2008.
Books
- Hullait H, Leslie DS, Pavlidis NG & King S (2020) Robust functional regression for outlier detection. In: Advanced Analytics and Learning on Temporal Data, Springer.
- Tarassenko L, Clifton DA, Bannister PR, King S & King D (2009) Novelty detection. In: Encyclopedia of Structural Health Monitoring, Wiley, p. 653-689.
